Handwriting Recognition Using Neural Networks
date post
11-Mar-2015Category
Documents
view
203download
3
Embed Size (px)
description
It will provides the details about Handwriting character recognition
Transcript of Handwriting Recognition Using Neural Networks
Handwriting Recognition using Neural Networks
KANTAIAH. CH Mtech(PTPG),SIT,JNTUH
Example Scanned Images
Handwriting Recognition Two different fields: Online Handwriting RecognitionWriter's pen movements captured Velocity, acceleration, stroke order etc. Style can be constrained (e.g. Graffitti gestures)
Offline Handwriting RecognitionOnly pixels Cannot constrain style (documents already written)
Offline is harder (less information) Genealogical records are all offline
Mary
Online Handwriting Recognition Modern systems are moderately successful, e.g. Microsoft Research's new Tablet PC:
Polynomial coefficients e.g. [0.94, 0.05, 0.29,...]
Offline Handwriting Recognition A difficult problem Almost as many approaches as there are researchers e.g. Pattern Recognition Statistical analysis Mathematical modelling Physics-based modelling Physics Subgraph matching / graph search Neural networks / machine learning Fractal image compression ... (too many to list) ...
Previous Work: Offline Online Conversion
Finding contour
Finding midline
Stroke ordering difficult problem
Offline Online Conversion ctd. Especially difficult with genealogical records:
Stroke ordering: difficult
Broken lines / blobs?
Not practical
Previous Work: Holistic Matching Whole word is stretched to match known words Sources of variation compound across word
Handwriting Recognition Some aspects of Handwriting Recognition:
Segmentation problem (can't read word until it is segmented; can't segment word until it is read) Different handwriting styles Use of dictionary to correct for errors in reading
nr? m?
Srnitb --> Smith
Thesis Approach: PreprocessingOutlines of word are traced and smoothed:
Handwriting slope is corrected for automatically:
Proposed System OverviewInputPre-processing (high curvature points) Segmentation
Dictionary
Character Recognizer
Recognition Engine
Context Models
Word or Number Candidates
Segmentation Goal: robustly cut letters into segments Match multiple segments to detect letters Easier than matching whole letter
Dynamic Global Search Assemble word spelling from possible letter readings
Best path: Williarw Suwkino (65% confidence)
Results (1)
Results (2)
Results (3)
Results (4)
In general: results even worse system only worked well on words it was specifically trained on
The Human Brain's Visual System
Retina
The Human Brain's Visual System
Angular edge detectors Retina
The Human Brain's Visual System
Line / curve detectors Angular edge detectors Retina
... ... ...
The Human Brain's Visual System
Feature detectors Line / curve detectors Angular edge detectors Retina ... ... ...
The Human Brain's Visual System
Lateral inhibition
Feature detectors Line / curve detectors Angular edge detectors Retina
Feedback
... ... ...
The Human Brain's Visual System
Letter / word shape recognizers Feature detectors Line / curve detectors Angular edge detectors Retina
JLateral inhibition Feedback
... ... ...
The Human Brain's Visual System
JosephLetter / word shape recognizers Feature detectors Line / curve detectors Angular edge detectors Retina
JLateral inhibition Feedback
... ... ...
Conclusions Handwriting recognition is important for genealogy... ...but it is hard Current methods don't work very well... ...and they don't operate much like the human brain Future work should focus on understanding the brain, and emulating it as much as possible, e.g. With: Hierarchical reasoning Feedback Lateral inhibition
Questions?
Thank You.
